# impoted the the library which is essential for data analysis
import pandas as pd

#reading the dataset from the loation
data=pd.read_csv("C:\\Users\\jafer\\Desktop\\yellow_tripdata_2015-01.csv")

#displaying first 5 values fromthe dataset
data.head()

	VendorID	tpep_pickup_datetime	tpep_dropoff_datetime	passenger_count	trip_distance	pickup_longitude	pickup_latitude	RateCodeID	dropoff_longitude	dropoff_latitude	payment_type	fare_amount	extra	mta_tax	tip_amount	tolls_amount	improvement_surcharge	total_amount
0	2	15/01/2015 19:05	15/01/2015 19:23	1	1.59	-73.993896	40.750111	1	-73.974785	40.750618	1	12.0	1.0	0.5	3.25	0.0	0.3	17.05
1	1	10/01/2015 20:33	10/01/2015 20:53	1	3.30	-74.001648	40.724243	1	-73.994415	40.759110	1	14.5	0.5	0.5	2.00	0.0	0.3	17.80
2	1	10/01/2015 20:33	10/01/2015 20:43	1	1.80	-73.963341	40.802788	1	-73.951820	40.824413	2	9.5	0.5	0.5	0.00	0.0	0.3	10.80
3	1	10/01/2015 20:33	10/01/2015 20:35	1	0.50	-74.009087	40.713818	1	-74.004326	40.719986	2	3.5	0.5	0.5	0.00	0.0	0.3	4.80
4	1	10/01/2015 20:33	10/01/2015 20:52	1	3.00	-73.971176	40.762428	1	-74.004181	40.742653	2	15.0	0.5	0.5	0.00	0.0	0.3	16.30

#displaying the shape od the dataset which is the number of rows and columns
#the number of rows are 12748986
#the number of columns are 19
data.shape

(572712, 18)

#which gives the complete information about the type of data which we have in our dataset
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 572712 entries, 0 to 572711
Data columns (total 18 columns):
 #   Column                 Non-Null Count   Dtype  
---  ------                 --------------   -----  
 0   VendorID               572712 non-null  int64  
 1   tpep_pickup_datetime   572712 non-null  object 
 2   tpep_dropoff_datetime  572712 non-null  object 
 3   passenger_count        572712 non-null  int64  
 4   trip_distance          572712 non-null  float64
 5   pickup_longitude       572712 non-null  float64
 6   pickup_latitude        572712 non-null  float64
 7   RateCodeID             572712 non-null  int64  
 8   dropoff_longitude      572712 non-null  float64
 9   dropoff_latitude       572712 non-null  float64
 10  payment_type           572712 non-null  int64  
 11  fare_amount            572712 non-null  float64
 12  extra                  572712 non-null  float64
 13  mta_tax                572712 non-null  float64
 14  tip_amount             572712 non-null  float64
 15  tolls_amount           572712 non-null  float64
 16  improvement_surcharge  572712 non-null  float64
 17  total_amount           572712 non-null  float64
dtypes: float64(12), int64(4), object(2)
memory usage: 78.7+ MB

#! pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import ydata_profiling
#import pandas_profiling
#from pandas_profiling import ProfileReport
from pandas_profiling import  ydata_profiling

data=pd.read_csv("C:\\Users\\jafer\\Desktop\\yellow_tripdata_2015-01.csv")

data

	VendorID	tpep_pickup_datetime	tpep_dropoff_datetime	passenger_count	trip_distance	pickup_longitude	pickup_latitude	RateCodeID	dropoff_longitude	dropoff_latitude	payment_type	fare_amount	extra	mta_tax	tip_amount	tolls_amount	improvement_surcharge	total_amount
0	2	15/01/2015 19:05	15/01/2015 19:23	1	1.59	-73.993896	40.750111	1	-73.974785	40.750618	1	12.0	1.0	0.5	3.25	0.0	0.3	17.05
1	1	10/01/2015 20:33	10/01/2015 20:53	1	3.30	-74.001648	40.724243	1	-73.994415	40.759110	1	14.5	0.5	0.5	2.00	0.0	0.3	17.80
2	1	10/01/2015 20:33	10/01/2015 20:43	1	1.80	-73.963341	40.802788	1	-73.951820	40.824413	2	9.5	0.5	0.5	0.00	0.0	0.3	10.80
3	1	10/01/2015 20:33	10/01/2015 20:35	1	0.50	-74.009087	40.713818	1	-74.004326	40.719986	2	3.5	0.5	0.5	0.00	0.0	0.3	4.80
4	1	10/01/2015 20:33	10/01/2015 20:52	1	3.00	-73.971176	40.762428	1	-74.004181	40.742653	2	15.0	0.5	0.5	0.00	0.0	0.3	16.30
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
572707	1	29/01/2015 19:55	29/01/2015 20:17	1	4.90	-74.001946	40.715950	1	-73.953331	40.771603	1	18.0	1.0	0.5	4.95	0.0	0.3	24.75
572708	1	29/01/2015 19:55	29/01/2015 20:01	1	1.20	-73.983826	40.749725	1	-73.995209	40.739670	2	6.0	0.5	0.5	0.00	0.0	0.3	7.30
572709	1	29/01/2015 19:55	29/01/2015 19:59	1	0.80	-73.966621	40.764755	1	-73.963760	40.773510	2	5.0	0.5	0.5	0.00	0.0	0.3	6.30
572710	1	29/01/2015 19:55	29/01/2015 19:59	1	0.90	-73.997810	40.756504	1	-73.987839	40.764717	1	5.0	1.0	0.5	1.35	0.0	0.3	8.15
572711	1	29/01/2015 19:55	29/01/2015 20:18	1	6.50	-73.952454	40.777096	1	-74.004990	40.731491	1	23.0	1.0	0.5	2.00	0.0	0.3	26.80

572712 rows × 18 columns

Profile=ProfileReport(data,title='Pandas Profiling Report',explorative=True)

Profile#.to_notebook_iframe()

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#Profile.to_file(output_file="output.html")
Profile.to_file("output.html")

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